# -*- coding: utf-8 -*-
from datetime import timedelta
from utils.operators.spark_sql_operator import SparkSqlOperator
from jms.ods.oms.yl_meeting_video_upload import jms_ods__yl_meeting_video_upload


testdingding_dt = SparkSqlOperator(
    task_id='testdingding_dt',
    task_concurrency=1,
    pool_slots=3,
    master='yarn',
    email=['payne.jiang@jtexpress.com','yl_bigdata@yl-scm.com'],
    name='testdingding_dt_{{ execution_date | date_add(1) | cst_ds }}',
    sql='jms/dwd/testdingding_dt/execute.hql',
    executor_cores=1,
    retries=0,
    executor_memory='20G',
    driver_memory='10g',
    num_executors=40,  # spark.dynamicAllocation.enabled 为 True 时，num_executors 表示最少 Executor 数
    conf={'spark.dynamicAllocation.enabled': 'true',  # 动态资源开启
          'spark.shuffle.service.enabled' : 'true',  # 动态资源 Shuffle 服务开启
          'spark.dynamicAllocation.maxExecutors'  : 50,  # 动态资源最大扩容 Executor 数
          'spark.dynamicAllocation.cachedExecutorIdleTimeout': 60,  # 动态资源自动释放闲置 Executor 的超时时间(s)
          'spark.sql.sources.partitionOverwriteMode' : 'dynamic',  # 允许删改已存在的分区
          'spark.executor.memoryOverhead'  : '8G',  # 堆外内存
          'spark.sql.shuffle.partitions'  : 200,
          'spark.default.parallelism' : 200,
          'spark.rpc.askTimeout' : 1200
          },
    hiveconf={'hive.exec.dynamic.partition' : 'true',  # 动态分区
              'hive.exec.dynamic.partition.mode' : 'nonstrict',
              'hive.exec.max.dynamic.partitions' : 400,  # 每天生成 20 个分区
              'hive.exec.max.dynamic.partitions.pernode': 400,  # 每天生成 20 个分区
              },
    yarn_queue='pro',
    execution_timeout=timedelta(minutes=30),
)




testdingding_dt << jms_ods__yl_meeting_video_upload
